Overview

Dataset statistics

Number of variables14
Number of observations17773
Missing cells33252
Missing cells (%)13.4%
Duplicate rows38
Duplicate rows (%)0.2%
Total size in memory2.5 MiB
Average record size in memory149.7 B

Variable types

Numeric13
Categorical1

Alerts

Dataset has 38 (0.2%) duplicate rowsDuplicates
Minimum Temperature is highly overall correlated with Maximum Temperature and 5 other fieldsHigh correlation
Maximum Temperature is highly overall correlated with Minimum Temperature and 5 other fieldsHigh correlation
Temperature is highly overall correlated with Minimum Temperature and 5 other fieldsHigh correlation
Dew Point is highly overall correlated with Minimum Temperature and 4 other fieldsHigh correlation
Relative Humidity is highly overall correlated with Minimum Temperature and 3 other fieldsHigh correlation
Wind Chill is highly overall correlated with Minimum Temperature and 4 other fieldsHigh correlation
Snow Depth is highly overall correlated with Minimum Temperature and 5 other fieldsHigh correlation
Visibility is highly overall correlated with Relative HumidityHigh correlation
Sea Level Pressure is highly overall correlated with Snow DepthHigh correlation
Wind Chill has 10051 (56.6%) missing valuesMissing
Snow Depth has 6781 (38.2%) missing valuesMissing
Visibility has 1384 (7.8%) missing valuesMissing
Cloud Cover has 14193 (79.9%) missing valuesMissing
Precipitation has 13849 (77.9%) zerosZeros
Snow Depth has 9943 (55.9%) zerosZeros
y has 8059 (45.3%) zerosZeros

Reproduction

Analysis started2022-12-18 19:55:18.566615
Analysis finished2022-12-18 19:55:35.963550
Duration17.4 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

Minimum Temperature
Real number (ℝ)

Distinct446
Distinct (%)2.5%
Missing48
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean10.393817
Minimum-13.2
Maximum38.2
Zeros26
Zeros (%)0.1%
Negative853
Negative (%)4.8%
Memory size277.7 KiB
2022-12-18T20:55:36.031210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-13.2
5-th percentile0.1
Q14.9
median10
Q315.6
95-th percentile22.5
Maximum38.2
Range51.4
Interquartile range (IQR)10.7

Descriptive statistics

Standard deviation7.1329548
Coefficient of variation (CV)0.68626906
Kurtosis-0.24037381
Mean10.393817
Median Absolute Deviation (MAD)5.3
Skewness0.21849668
Sum184230.4
Variance50.879044
MonotonicityNot monotonic
2022-12-18T20:55:36.136158image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1 113
 
0.6%
6.4 107
 
0.6%
3.1 106
 
0.6%
9.1 106
 
0.6%
11.9 105
 
0.6%
9.2 104
 
0.6%
8.4 104
 
0.6%
9.6 101
 
0.6%
8 100
 
0.6%
13.4 100
 
0.6%
Other values (436) 16679
93.8%
ValueCountFrequency (%)
-13.2 1
< 0.1%
-12.8 2
< 0.1%
-12.5 1
< 0.1%
-12.4 1
< 0.1%
-12.1 1
< 0.1%
-11.7 1
< 0.1%
-11.6 2
< 0.1%
-11.4 1
< 0.1%
-11.3 1
< 0.1%
-11.2 1
< 0.1%
ValueCountFrequency (%)
38.2 1
< 0.1%
37.9 1
< 0.1%
37.1 1
< 0.1%
35.3 1
< 0.1%
35.1 1
< 0.1%
34.9 1
< 0.1%
34.5 1
< 0.1%
34 2
< 0.1%
33.9 1
< 0.1%
33.7 2
< 0.1%

Maximum Temperature
Real number (ℝ)

Distinct446
Distinct (%)2.5%
Missing48
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean10.393817
Minimum-13.2
Maximum38.2
Zeros26
Zeros (%)0.1%
Negative853
Negative (%)4.8%
Memory size277.7 KiB
2022-12-18T20:55:36.227373image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-13.2
5-th percentile0.1
Q14.9
median10
Q315.6
95-th percentile22.5
Maximum38.2
Range51.4
Interquartile range (IQR)10.7

Descriptive statistics

Standard deviation7.1329548
Coefficient of variation (CV)0.68626906
Kurtosis-0.24037381
Mean10.393817
Median Absolute Deviation (MAD)5.3
Skewness0.21849668
Sum184230.4
Variance50.879044
MonotonicityNot monotonic
2022-12-18T20:55:36.329845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1 113
 
0.6%
6.4 107
 
0.6%
3.1 106
 
0.6%
9.1 106
 
0.6%
11.9 105
 
0.6%
9.2 104
 
0.6%
8.4 104
 
0.6%
9.6 101
 
0.6%
8 100
 
0.6%
13.4 100
 
0.6%
Other values (436) 16679
93.8%
ValueCountFrequency (%)
-13.2 1
< 0.1%
-12.8 2
< 0.1%
-12.5 1
< 0.1%
-12.4 1
< 0.1%
-12.1 1
< 0.1%
-11.7 1
< 0.1%
-11.6 2
< 0.1%
-11.4 1
< 0.1%
-11.3 1
< 0.1%
-11.2 1
< 0.1%
ValueCountFrequency (%)
38.2 1
< 0.1%
37.9 1
< 0.1%
37.1 1
< 0.1%
35.3 1
< 0.1%
35.1 1
< 0.1%
34.9 1
< 0.1%
34.5 1
< 0.1%
34 2
< 0.1%
33.9 1
< 0.1%
33.7 2
< 0.1%

Temperature
Real number (ℝ)

Distinct446
Distinct (%)2.5%
Missing48
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean10.393817
Minimum-13.2
Maximum38.2
Zeros26
Zeros (%)0.1%
Negative853
Negative (%)4.8%
Memory size277.7 KiB
2022-12-18T20:55:36.421885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-13.2
5-th percentile0.1
Q14.9
median10
Q315.6
95-th percentile22.5
Maximum38.2
Range51.4
Interquartile range (IQR)10.7

Descriptive statistics

Standard deviation7.1329548
Coefficient of variation (CV)0.68626906
Kurtosis-0.24037381
Mean10.393817
Median Absolute Deviation (MAD)5.3
Skewness0.21849668
Sum184230.4
Variance50.879044
MonotonicityNot monotonic
2022-12-18T20:55:36.524161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.1 113
 
0.6%
6.4 107
 
0.6%
3.1 106
 
0.6%
9.1 106
 
0.6%
11.9 105
 
0.6%
9.2 104
 
0.6%
8.4 104
 
0.6%
9.6 101
 
0.6%
8 100
 
0.6%
13.4 100
 
0.6%
Other values (436) 16679
93.8%
ValueCountFrequency (%)
-13.2 1
< 0.1%
-12.8 2
< 0.1%
-12.5 1
< 0.1%
-12.4 1
< 0.1%
-12.1 1
< 0.1%
-11.7 1
< 0.1%
-11.6 2
< 0.1%
-11.4 1
< 0.1%
-11.3 1
< 0.1%
-11.2 1
< 0.1%
ValueCountFrequency (%)
38.2 1
< 0.1%
37.9 1
< 0.1%
37.1 1
< 0.1%
35.3 1
< 0.1%
35.1 1
< 0.1%
34.9 1
< 0.1%
34.5 1
< 0.1%
34 2
< 0.1%
33.9 1
< 0.1%
33.7 2
< 0.1%

Dew Point
Real number (ℝ)

Distinct345
Distinct (%)2.0%
Missing120
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean6.4477029
Minimum-14.6
Maximum21
Zeros112
Zeros (%)0.6%
Negative2335
Negative (%)13.1%
Memory size277.7 KiB
2022-12-18T20:55:36.624415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-14.6
5-th percentile-3.5
Q12.1
median6.8
Q310.9
95-th percentile15.7
Maximum21
Range35.6
Interquartile range (IQR)8.8

Descriptive statistics

Standard deviation6.0195496
Coefficient of variation (CV)0.93359599
Kurtosis-0.24269251
Mean6.4477029
Median Absolute Deviation (MAD)4.5
Skewness-0.29686343
Sum113821.3
Variance36.234977
MonotonicityNot monotonic
2022-12-18T20:55:36.716448image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 139
 
0.8%
1.6 128
 
0.7%
9.5 126
 
0.7%
2.8 125
 
0.7%
10 122
 
0.7%
8.2 119
 
0.7%
9.8 118
 
0.7%
9.6 117
 
0.7%
8.9 115
 
0.6%
9.7 115
 
0.6%
Other values (335) 16429
92.4%
(Missing) 120
 
0.7%
ValueCountFrequency (%)
-14.6 1
 
< 0.1%
-14.1 1
 
< 0.1%
-14 1
 
< 0.1%
-13.9 2
 
< 0.1%
-13.8 1
 
< 0.1%
-13.5 1
 
< 0.1%
-13.3 2
 
< 0.1%
-13.2 2
 
< 0.1%
-13.1 1
 
< 0.1%
-12.9 6
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20.9 1
 
< 0.1%
20.8 1
 
< 0.1%
20.6 1
 
< 0.1%
20.4 2
 
< 0.1%
20.2 5
< 0.1%
20.1 2
 
< 0.1%
20 3
< 0.1%
19.9 1
 
< 0.1%
19.8 3
< 0.1%

Relative Humidity
Real number (ℝ)

Distinct5424
Distinct (%)30.7%
Missing120
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean78.605042
Minimum18.7
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.7 KiB
2022-12-18T20:55:36.818951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum18.7
5-th percentile46.47
Q168.64
median82.93
Q391.59
95-th percentile97.12
Maximum100
Range81.3
Interquartile range (IQR)22.95

Descriptive statistics

Standard deviation16.060288
Coefficient of variation (CV)0.20431626
Kurtosis0.032139079
Mean78.605042
Median Absolute Deviation (MAD)10.19
Skewness-0.90235993
Sum1387614.8
Variance257.93284
MonotonicityNot monotonic
2022-12-18T20:55:36.913343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93.91 16
 
0.1%
93.1 15
 
0.1%
92.78 15
 
0.1%
94.59 15
 
0.1%
95.12 14
 
0.1%
93.46 13
 
0.1%
91.87 13
 
0.1%
94.89 13
 
0.1%
93.57 13
 
0.1%
95.62 13
 
0.1%
Other values (5414) 17513
98.5%
(Missing) 120
 
0.7%
ValueCountFrequency (%)
18.7 1
< 0.1%
19.35 1
< 0.1%
21.9 1
< 0.1%
21.99 1
< 0.1%
22.38 1
< 0.1%
23.31 1
< 0.1%
23.41 1
< 0.1%
24.26 1
< 0.1%
24.27 1
< 0.1%
25.33 1
< 0.1%
ValueCountFrequency (%)
100 12
0.1%
99.97 6
< 0.1%
99.96 2
 
< 0.1%
99.95 2
 
< 0.1%
99.93 2
 
< 0.1%
99.92 1
 
< 0.1%
99.89 3
 
< 0.1%
99.88 1
 
< 0.1%
99.87 1
 
< 0.1%
99.85 1
 
< 0.1%

Wind Speed
Real number (ℝ)

Distinct453
Distinct (%)2.6%
Missing145
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean13.894424
Minimum0.1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.7 KiB
2022-12-18T20:55:37.015627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile3
Q18.2
median12.8
Q318.4
95-th percentile28.6
Maximum55
Range54.9
Interquartile range (IQR)10.2

Descriptive statistics

Standard deviation7.8205459
Coefficient of variation (CV)0.56285501
Kurtosis0.91121169
Mean13.894424
Median Absolute Deviation (MAD)5
Skewness0.83418643
Sum244930.9
Variance61.160938
MonotonicityNot monotonic
2022-12-18T20:55:37.107698image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.6 119
 
0.7%
14.4 115
 
0.6%
13.2 114
 
0.6%
13.6 113
 
0.6%
11.9 112
 
0.6%
11.6 112
 
0.6%
11.8 111
 
0.6%
10.4 110
 
0.6%
11.2 110
 
0.6%
13.8 110
 
0.6%
Other values (443) 16502
92.8%
(Missing) 145
 
0.8%
ValueCountFrequency (%)
0.1 3
 
< 0.1%
0.2 9
0.1%
0.3 2
 
< 0.1%
0.4 12
0.1%
0.5 13
0.1%
0.6 8
< 0.1%
0.7 8
< 0.1%
0.8 13
0.1%
0.9 8
< 0.1%
1 14
0.1%
ValueCountFrequency (%)
55 1
< 0.1%
53.9 1
< 0.1%
53.3 1
< 0.1%
53 2
< 0.1%
52.9 2
< 0.1%
51.2 1
< 0.1%
50.2 1
< 0.1%
50.1 1
< 0.1%
50 1
< 0.1%
49.2 1
< 0.1%

Wind Direction
Real number (ℝ)

Distinct353
Distinct (%)2.0%
Missing145
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean203.2213
Minimum1
Maximum360
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.7 KiB
2022-12-18T20:55:37.199626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1131
median224
Q3279
95-th percentile324
Maximum360
Range359
Interquartile range (IQR)148

Descriptive statistics

Standard deviation90.198712
Coefficient of variation (CV)0.44384478
Kurtosis-0.90615066
Mean203.2213
Median Absolute Deviation (MAD)67
Skewness-0.44029223
Sum3582385
Variance8135.8077
MonotonicityNot monotonic
2022-12-18T20:55:37.291804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
230 347
 
2.0%
229 342
 
1.9%
290 314
 
1.8%
220 313
 
1.8%
239 284
 
1.6%
250 267
 
1.5%
300 252
 
1.4%
240 226
 
1.3%
260 225
 
1.3%
219 219
 
1.2%
Other values (343) 14839
83.5%
ValueCountFrequency (%)
1 19
0.1%
2 4
 
< 0.1%
3 21
0.1%
4 20
0.1%
5 6
 
< 0.1%
6 18
0.1%
7 22
0.1%
8 4
 
< 0.1%
9 21
0.1%
10 34
0.2%
ValueCountFrequency (%)
360 32
0.2%
359 22
0.1%
357 13
 
0.1%
356 16
0.1%
355 14
 
0.1%
354 7
 
< 0.1%
353 26
0.1%
352 5
 
< 0.1%
351 23
0.1%
350 35
0.2%

Wind Chill
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct237
Distinct (%)3.1%
Missing10051
Missing (%)56.6%
Infinite0
Infinite (%)0.0%
Mean1.6888889
Minimum-15.5
Maximum9.7
Zeros44
Zeros (%)0.2%
Negative2492
Negative (%)14.0%
Memory size277.7 KiB
2022-12-18T20:55:37.394222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-15.5
5-th percentile-5.1
Q1-1.1
median1.9
Q34.9
95-th percentile7.5
Maximum9.7
Range25.2
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.123516
Coefficient of variation (CV)2.4415555
Kurtosis0.48745001
Mean1.6888889
Median Absolute Deviation (MAD)3
Skewness-0.61871089
Sum13041.6
Variance17.003384
MonotonicityNot monotonic
2022-12-18T20:55:37.497347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 86
 
0.5%
2.1 85
 
0.5%
0.8 85
 
0.5%
1.4 85
 
0.5%
2 81
 
0.5%
3.5 79
 
0.4%
1.9 79
 
0.4%
1 78
 
0.4%
-2.3 76
 
0.4%
6.7 74
 
0.4%
Other values (227) 6914
38.9%
(Missing) 10051
56.6%
ValueCountFrequency (%)
-15.5 1
< 0.1%
-15.1 1
< 0.1%
-15 1
< 0.1%
-14.9 1
< 0.1%
-14.4 2
< 0.1%
-14.2 1
< 0.1%
-14 1
< 0.1%
-13.9 1
< 0.1%
-13.7 1
< 0.1%
-13.5 1
< 0.1%
ValueCountFrequency (%)
9.7 1
 
< 0.1%
9.5 1
 
< 0.1%
9.3 2
 
< 0.1%
9.2 5
 
< 0.1%
9.1 8
< 0.1%
9 7
< 0.1%
8.9 11
0.1%
8.8 15
0.1%
8.7 11
0.1%
8.6 15
0.1%

Precipitation
Real number (ℝ)

Distinct298
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.083767513
Minimum0
Maximum9.02
Zeros13849
Zeros (%)77.9%
Negative0
Negative (%)0.0%
Memory size277.7 KiB
2022-12-18T20:55:37.597781image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.47
Maximum9.02
Range9.02
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.35540161
Coefficient of variation (CV)4.2427141
Kurtosis104.28442
Mean0.083767513
Median Absolute Deviation (MAD)0
Skewness8.36545
Sum1488.8
Variance0.1263103
MonotonicityNot monotonic
2022-12-18T20:55:37.692052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13849
77.9%
0.01 602
 
3.4%
0.02 270
 
1.5%
0.05 197
 
1.1%
0.03 194
 
1.1%
0.04 143
 
0.8%
0.06 122
 
0.7%
0.08 105
 
0.6%
0.07 94
 
0.5%
0.09 79
 
0.4%
Other values (288) 2118
 
11.9%
ValueCountFrequency (%)
0 13849
77.9%
0.01 602
 
3.4%
0.02 270
 
1.5%
0.03 194
 
1.1%
0.04 143
 
0.8%
0.05 197
 
1.1%
0.06 122
 
0.7%
0.07 94
 
0.5%
0.08 105
 
0.6%
0.09 79
 
0.4%
ValueCountFrequency (%)
9.02 1
< 0.1%
7.87 1
< 0.1%
7.54 1
< 0.1%
5.77 1
< 0.1%
5.73 1
< 0.1%
5.3 1
< 0.1%
5.18 1
< 0.1%
5.01 1
< 0.1%
4.99 1
< 0.1%
4.96 1
< 0.1%

Snow Depth
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct378
Distinct (%)3.4%
Missing6781
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean0.17686226
Minimum0
Maximum6.37
Zeros9943
Zeros (%)55.9%
Negative0
Negative (%)0.0%
Memory size277.7 KiB
2022-12-18T20:55:37.785595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6.37
Range6.37
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.72921681
Coefficient of variation (CV)4.1230774
Kurtosis28.565441
Mean0.17686226
Median Absolute Deviation (MAD)0
Skewness5.1128458
Sum1944.07
Variance0.53175715
MonotonicityNot monotonic
2022-12-18T20:55:37.879650image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9943
55.9%
3 52
 
0.3%
2 50
 
0.3%
1 35
 
0.2%
0.54 18
 
0.1%
0.25 17
 
0.1%
0.33 17
 
0.1%
0.83 17
 
0.1%
0.79 17
 
0.1%
0.67 17
 
0.1%
Other values (368) 809
 
4.6%
(Missing) 6781
38.2%
ValueCountFrequency (%)
0 9943
55.9%
0.04 14
 
0.1%
0.05 2
 
< 0.1%
0.08 15
 
0.1%
0.09 1
 
< 0.1%
0.1 2
 
< 0.1%
0.13 14
 
0.1%
0.15 3
 
< 0.1%
0.17 15
 
0.1%
0.18 2
 
< 0.1%
ValueCountFrequency (%)
6.37 1
< 0.1%
6.35 1
< 0.1%
6.33 1
< 0.1%
6.3 1
< 0.1%
6.28 2
< 0.1%
6.26 1
< 0.1%
6.24 1
< 0.1%
6.22 1
< 0.1%
6.2 1
< 0.1%
6.18 1
< 0.1%

Visibility
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct481
Distinct (%)2.9%
Missing1384
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean27.009323
Minimum0
Maximum50
Zeros103
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size277.7 KiB
2022-12-18T20:55:37.973776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.6
Q113.5
median28.5
Q340
95-th percentile50
Maximum50
Range50
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation14.738858
Coefficient of variation (CV)0.5456952
Kurtosis-1.2637
Mean27.009323
Median Absolute Deviation (MAD)12.5
Skewness-0.11118611
Sum442655.8
Variance217.23394
MonotonicityNot monotonic
2022-12-18T20:55:38.078225image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 821
 
4.6%
40 698
 
3.9%
45 616
 
3.5%
30 173
 
1.0%
35 165
 
0.9%
41 125
 
0.7%
5 111
 
0.6%
25 105
 
0.6%
0 103
 
0.6%
40.7 98
 
0.6%
Other values (471) 13374
75.2%
(Missing) 1384
 
7.8%
ValueCountFrequency (%)
0 103
0.6%
0.1 13
 
0.1%
0.3 1
 
< 0.1%
0.7 1
 
< 0.1%
0.9 94
0.5%
1 45
0.3%
1.1 6
 
< 0.1%
1.3 4
 
< 0.1%
1.4 3
 
< 0.1%
1.6 2
 
< 0.1%
ValueCountFrequency (%)
50 821
4.6%
49.9 37
 
0.2%
49.7 39
 
0.2%
49.6 36
 
0.2%
49.4 28
 
0.2%
49.3 35
 
0.2%
49.1 25
 
0.1%
49 91
 
0.5%
48.9 1
 
< 0.1%
48.8 13
 
0.1%

Cloud Cover
Categorical

Distinct4
Distinct (%)0.1%
Missing14193
Missing (%)79.9%
Memory size904.4 KiB
0.0
2073 
11.1
740 
13.0
712 
1.9
 
55

Length

Max length4
Median length3
Mean length3.4055866
Min length3

Characters and Unicode

Total characters12192
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11.1
2nd row13.0
3rd row13.0
4th row13.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 2073
 
11.7%
11.1 740
 
4.2%
13.0 712
 
4.0%
1.9 55
 
0.3%
(Missing) 14193
79.9%

Length

2022-12-18T20:55:38.161811image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-18T20:55:38.255992image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 2073
57.9%
11.1 740
 
20.7%
13.0 712
 
19.9%
1.9 55
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 4858
39.8%
. 3580
29.4%
1 2987
24.5%
3 712
 
5.8%
9 55
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8612
70.6%
Other Punctuation 3580
29.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4858
56.4%
1 2987
34.7%
3 712
 
8.3%
9 55
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 3580
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12192
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4858
39.8%
. 3580
29.4%
1 2987
24.5%
3 712
 
5.8%
9 55
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4858
39.8%
. 3580
29.4%
1 2987
24.5%
3 712
 
5.8%
9 55
 
0.5%

Sea Level Pressure
Real number (ℝ)

Distinct644
Distinct (%)3.7%
Missing169
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean1015.7098
Minimum977.6
Maximum1048.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.7 KiB
2022-12-18T20:55:38.339407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum977.6
5-th percentile997.9
Q11009.6
median1016.8
Q31022.1
95-th percentile1030.8
Maximum1048.2
Range70.6
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation10.043681
Coefficient of variation (CV)0.0098883378
Kurtosis0.63333519
Mean1015.7098
Median Absolute Deviation (MAD)6.1
Skewness-0.43952496
Sum17880554
Variance100.87553
MonotonicityNot monotonic
2022-12-18T20:55:38.433407image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1019.1 109
 
0.6%
1019.9 108
 
0.6%
1019.6 104
 
0.6%
1017.8 101
 
0.6%
1018.4 100
 
0.6%
1021.4 99
 
0.6%
1018.5 99
 
0.6%
1018.9 98
 
0.6%
1019.3 98
 
0.6%
1019.2 97
 
0.5%
Other values (634) 16591
93.3%
(Missing) 169
 
1.0%
ValueCountFrequency (%)
977.6 1
 
< 0.1%
977.7 2
< 0.1%
977.8 1
 
< 0.1%
977.9 1
 
< 0.1%
978.1 2
< 0.1%
978.3 1
 
< 0.1%
978.4 1
 
< 0.1%
978.5 1
 
< 0.1%
978.7 1
 
< 0.1%
978.9 3
< 0.1%
ValueCountFrequency (%)
1048.2 1
 
< 0.1%
1048.1 1
 
< 0.1%
1048 2
< 0.1%
1047.9 1
 
< 0.1%
1047.8 4
< 0.1%
1047.7 2
< 0.1%
1047.6 2
< 0.1%
1047.2 2
< 0.1%
1046.6 1
 
< 0.1%
1046.5 1
 
< 0.1%

y
Real number (ℝ)

Distinct199
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0010127722
Minimum-100
Maximum100
Zeros8059
Zeros (%)45.3%
Negative4846
Negative (%)27.3%
Memory size277.7 KiB
2022-12-18T20:55:38.548333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-17
Q1-2
median0
Q32
95-th percentile17
Maximum100
Range200
Interquartile range (IQR)4

Descriptive statistics

Standard deviation17.471133
Coefficient of variation (CV)17250.803
Kurtosis16.075643
Mean0.0010127722
Median Absolute Deviation (MAD)2
Skewness0.016380756
Sum18
Variance305.24049
MonotonicityNot monotonic
2022-12-18T20:55:38.642345image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8059
45.3%
3 598
 
3.4%
-3 592
 
3.3%
-4 529
 
3.0%
4 503
 
2.8%
-5 472
 
2.7%
2 469
 
2.6%
5 460
 
2.6%
-2 437
 
2.5%
6 373
 
2.1%
Other values (189) 5281
29.7%
ValueCountFrequency (%)
-100 88
0.5%
-99 1
 
< 0.1%
-98 3
 
< 0.1%
-97 1
 
< 0.1%
-96 1
 
< 0.1%
-95 2
 
< 0.1%
-94 1
 
< 0.1%
-93 2
 
< 0.1%
-92 2
 
< 0.1%
-91 1
 
< 0.1%
ValueCountFrequency (%)
100 92
0.5%
99 2
 
< 0.1%
98 3
 
< 0.1%
96 3
 
< 0.1%
95 2
 
< 0.1%
93 2
 
< 0.1%
92 3
 
< 0.1%
91 3
 
< 0.1%
90 2
 
< 0.1%
89 2
 
< 0.1%

Interactions

2022-12-18T20:55:34.268573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:19.530909image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:20.816619image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:22.110819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:23.266425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:24.939250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:26.110263image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:27.213817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:28.371215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:29.608622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:30.680604image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:31.794509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:32.896758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:34.360656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:19.646181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:20.934746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:22.185020image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:23.371818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:25.024080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:26.194667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:27.291033image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:28.439444image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:29.682195image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:30.765347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:31.865065image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:32.981204image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:34.442546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:19.737485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:21.048433image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:22.273847image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:23.460042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:25.108351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:26.284313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:27.389476image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:28.524230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:29.772355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:30.843440image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:31.948353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:33.065498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:34.534501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:19.834575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:21.149842image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:22.343044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:23.530217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:25.208361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:26.366254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:27.467139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:28.602377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:29.846539image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:30.912646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:32.027481image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:33.149644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:34.628370image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:19.936581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:21.248127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:22.443099image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:23.630732image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:25.292941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:26.449825image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:27.567509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:28.852294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:29.924752image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:31.013088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:32.126845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:33.249583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:34.722955image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:20.040392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:21.350034image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:22.527916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:23.714917image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:25.392929image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:26.543492image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:27.666395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:28.933555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:30.025227image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:31.097751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:32.211500image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:33.350071image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:34.807459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:20.133846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:21.445063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:22.612546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:23.815072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:25.477762image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:26.616488image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:27.752715image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:29.006242image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:30.094353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:31.182518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:32.296413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:33.428208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:34.897262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:20.245152image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:21.549021image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:22.690777image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:23.904050image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:25.562474image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:26.709666image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:27.837553image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:29.091063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:30.179170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:31.271567image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:32.380714image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:33.528184image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:34.981598image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:20.332485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:21.649236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:22.776454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:23.977851image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:25.662686image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:26.782359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:27.922255image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:29.175874image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:30.264068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:31.353659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:32.465056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:33.612953image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:35.065904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:20.415646image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:21.730768image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:22.883244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:24.062573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:25.740897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:26.867176image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:28.000390image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:29.260713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:30.342194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:31.436577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:32.549791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:33.701828image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:35.150163image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:20.514443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:21.844414image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:22.988684image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:24.147459image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:25.825268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:26.951428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:28.084801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:29.345420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:30.428044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:31.526761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:32.627985image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:33.994611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:35.234416image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:20.604088image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:21.926136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:23.082528image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:24.750791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:25.910074image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:27.020022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:28.169617image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:29.426473image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:30.495829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:31.601244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:32.712324image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:34.086580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:35.334279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:20.722429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:22.010900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:23.176526image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:24.849421image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:26.025042image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:27.119977image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:28.270035image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:29.509063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:30.595766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:31.698571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:32.796663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-18T20:55:34.176571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-18T20:55:38.995144image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-18T20:55:39.138130image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-18T20:55:39.278795image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-18T20:55:39.413495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-18T20:55:35.465987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-18T20:55:35.649015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-18T20:55:35.832895image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Minimum TemperatureMaximum TemperatureTemperatureDew PointRelative HumidityWind SpeedWind DirectionWind ChillPrecipitationSnow DepthVisibilityCloud CoverSea Level Pressurey
2020-10-01 00:00:0012.312.312.311.192.438.8120.0NaN0.00.017.711.11012.70
2020-10-01 01:00:0011.811.811.810.692.7910.1106.0NaN0.00.015.113.01012.10
2020-10-01 02:00:0011.211.211.210.293.608.8120.0NaN0.00.014.713.01011.60
2020-10-01 03:00:0010.510.510.59.693.646.0110.0NaN0.00.014.113.01011.1-38
2020-10-01 04:00:0010.310.310.39.494.158.6127.0NaN0.00.014.00.01010.518
2020-10-01 05:00:0010.210.210.29.494.5411.0111.0NaN0.00.014.40.01009.8-9
2020-10-01 06:00:009.89.89.89.094.8810.0116.08.40.00.012.91.91009.30
2020-10-01 07:00:009.79.79.78.893.9914.8100.07.60.00.012.00.01008.70
2020-10-01 08:00:009.79.79.78.793.5812.6107.07.90.00.012.111.11008.4-11
2020-10-01 09:00:0010.710.710.79.391.1613.5109.0NaN0.00.012.10.01008.0-4
Minimum TemperatureMaximum TemperatureTemperatureDew PointRelative HumidityWind SpeedWind DirectionWind ChillPrecipitationSnow DepthVisibilityCloud CoverSea Level Pressurey
2022-10-09 15:00:0015.015.015.07.159.0412.3200.0NaN0.0NaN49.0NaN1024.2-7
2022-10-09 16:00:0015.315.315.37.158.1212.9179.0NaN0.0NaNNaNNaN1023.50
2022-10-09 17:00:0014.814.814.86.657.7816.1171.0NaN0.0NaNNaN11.11022.80
2022-10-09 18:00:0013.713.713.77.264.5914.4150.0NaN0.0NaNNaN0.01022.1-7
2022-10-09 19:00:0011.311.311.37.275.6612.0139.0NaN0.0NaN28.0NaN1021.8-6
2022-10-09 20:00:009.89.89.87.082.658.3103.08.80.0NaN40.6NaN1021.66
2022-10-09 21:00:008.98.98.96.786.247.9103.07.70.0NaN46.913.01021.20
2022-10-09 22:00:009.29.29.26.583.4011.4120.07.50.0NaN42.9NaN1020.80
2022-10-09 23:00:009.39.39.36.481.7514.3131.07.20.0NaN46.0NaN1020.40
2022-10-10 00:00:009.09.09.06.081.3915.8147.06.60.0NaN49.0NaN1020.00

Duplicate rows

Most frequently occurring

Minimum TemperatureMaximum TemperatureTemperatureDew PointRelative HumidityWind SpeedWind DirectionWind ChillPrecipitationSnow DepthVisibilityCloud CoverSea Level Pressurey# duplicates
34NaNNaNNaNNaNNaNNaNNaNNaN0.00NaNNaNNaNNaN022
0-1.8-1.8-1.8-2.892.771.8143.0NaN0.00NaN24.10.01032.602
1-1.3-1.3-1.3-1.995.235.195.0-3.10.00NaN8.30.01024.2-122
21.21.21.2-0.985.89NaNNaNNaN0.000.0NaN11.1NaN-422
33.53.53.53.197.428.0249.01.40.000.03.9NaN1006.5282
44.14.14.13.193.5611.2151.01.30.000.025.6NaN1018.602
55.35.35.34.896.7311.9271.02.60.06NaN6.7NaN1031.302
65.45.45.42.782.599.9260.03.20.050.035.113.01016.0-82
76.26.26.25.998.258.8249.04.30.020.08.5NaN1026.2302
86.66.66.61.368.992.082.0NaN0.00NaN14.90.01031.5612